Harnessing AI & Data to Unlock Alpha in Private & Structured Credit: Key Takeaways from Our Breakfast Roundtable

An emphasis on Risk Management

Reflecting broader industry priorities, risk management emerged as the highest-impact domain for AI deployment, with 40% of attendees citing it as the most compelling area to augment through AI-driven insights. Pipeline analytics (30%), credit evaluation (20%), and research acceleration (10%) rounded out the focus areas.  

The pipeline-to-underwrite continuum remains ripe for AI augmentation. 

Persona-driven approaches

Return on investment discussions emphasized that beyond cost takeout, AI is reshaping the operational DNA of credit management. Leading firms are adopting persona-driven approaches – redefining portfolio managers’, credit analysts’, and fund controllers’ workflows – and envisioning an AI-augmented workforce that fundamentally transforms credit decision-making and portfolio monitoring. 

Looking ahead, the roundtable explored the transformed role of Investor Relations professionals in credit firms – moving beyond static PDF reports towards delivering clean, curated, and interactive credit portfolio data.  

Picture real-time drilldowns into tranche-level performance, covenant monitoring, and risk exposures, accessible directly via secure tablets during LP discussions. 

Data paves the way

Data model alignment remains a critical operational challenge, especially given the breadth of sub-asset classes within private credit (e.g., direct lending, specialty finance, mezzanine, structured credit). As portfolios diversify, achieving consistency and interoperability across heterogeneous data sets becomes increasingly complex.  

Many firms are leveraging external expertise to harmonize internal data taxonomies with those of administrators and external counterparties. This shift reflects an evolution toward an integrated operating model where credit managers and administrators collaborate seamlessly, facilitating near real-time and secure data exchange.  

The alignment of internal and external credit data models is paramount to maintaining robust controls and preserving the integrity of credit risk frameworks. 

In fact, access to relevant, high-quality credit data surfaced as the most significant implementation barrier, cited by 70% of attendees.  

Skilled personnel with credit and data science expertise were identified by 30% as a bottleneck, underscoring human capital’s critical role alongside technology investments.  

Interestingly, infrastructure constraints like processing power were negligible concerns for all those in attendance. 

With great data, comes great responsibility

While fully transparent ‘direct data access’ models remain premature – given the need for rigorous data governance, auditability, and contractual safeguards – credit managers are pioneering richer, interactive, and relationship-driven data delivery models that enhance LP engagement and trust. 

AI-powered portfolio monitoring platforms and credit analytics tools are increasingly putting credit portfolio managers and risk teams in control of master data definitions and mappings, reinforcing stringent protocols amid growing data complexity. 

This dynamic dialogue reaffirmed that AI and data are not ancillary technologies but strategic enablers driving the next frontier of value creation in private and structured credit.  

How can we help? 

Alpha Alternatives and Broadridge are proud to support credit managers on this transformative journey. 

If you’re passionate about harnessing AI and next-generation data capabilities to enhance credit origination, underwriting, and portfolio monitoring, we’d welcome the opportunity to connect.